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This open access book is meant as a textbook for Computer Science students who are looking for a gentle introduction to the world of quantum computing. More specifically, it is written for readers who have basic knowledge of Artificial Intelligence (AI) and Machine Learning (ML) and have a certain familiarity with search algorithms, optimization techniques, and neural networks. This is not because the authors are interested in Quantum AI or Quantum ML, but because they start from the basic premise that there exists a conceptual bridge between certain AI/ML models and certain quantum computing models.
The purpose of this book is therefore 1) to revisit these AI/ML models and their applications, and 2) to build on this familiar foundation to segue into the study of quantum computing and its possible use cases. The presentation is technical but pragmatic and practice oriented. The authors cover theory to the necessary extent but largely proceed in an example-driven manner. Most of the examples are concerned with combinatorial optimization and consider problems that can be cast as quadratic unconstrained binary optimization problems.
Numerous python/numpy/scipy codes support the mathematical discussion and demonstrate how to put theory into practice, accompanied by exercises for each chapter. Parts of the material were adopted from long running lectures on pattern recognition, on the foundations of quantum computing, and on quantum computing algorithms, which are taught by the authors in the Computer Science master’s program at the University of Bonn.
List of contents
Preface.- Part I. Preliminaries.- 1. Setting the Stage.- 2. Boolean Domains, Numbers, and Vectors.- 3. Kronecker Products.- 4. QUBOs.- 5. QUBO Models.- Part II. Hopfield Nets.- 6. Hopfield Nets.- 7. Hopfield Nets in Action.- 8. Hopfield Nets and Statistical Mechanics.- 9. Hopfield Nets and Quantum Mechanics.- Part III. Quantum Computing.- 10. Quantum Mechanics in a Nutshell.- 11. Exploring Quantum Weirdness.- 12. Adiabatic Quantum Computing.- 13. An Outlook to Quantum Gate Computing.- Index.
About the author
Christian Bauckhage is a Professor for Intelligent Learning Systems at the University of Bonn, Lead Scientist for Machine Learning at Fraunhofer IAIS, and co-director of The Lamarr Institute for Machine Learning and Artificial Intelligence. His experience in the fields of Data Science and Artificial Intelligence results from more than 20 years of applied and basic research in industry and academia. Currently, Prof. Bauckhage is largely concerned with the topic of Quantum Computing and the possibilities created by quantum computers to tackle previously almost unsolvable processes in Artificial Intelligence and Machine Learning. Since he has a background in computer science and theoretical physics, his lectures on quantum computing frequently combine both perspectives.
Rafet Sifa is a Professor for Applied Machine Learning at the University of Bonn and Head of the Hybrid Intelligence Department at Fraunhofer IAIS. His current research focuses on hybrid, interpretable, and resource-aware learning systems for text mining, behavioural analytics, digital forensics, and medical informatics. Prof. Sifa has more than 15 years of experience with the development and deployment of machine learning models for real-world applications at scale. His research on quantum computing solutions is motivated by practical problems in business optimization, finance, and accounting.
Summary
This open access book is meant as a textbook for Computer Science students who are looking for a gentle introduction to the world of quantum computing. More specifically, it is written for readers who have basic knowledge of Artificial Intelligence (AI) and Machine Learning (ML) and have a certain familiarity with search algorithms, optimization techniques, and neural networks. This is not because the authors are interested in Quantum AI or Quantum ML, but because they start from the basic premise that there exists a conceptual bridge between certain AI/ML models and certain quantum computing models.
The purpose of this book is therefore 1) to revisit these AI/ML models and their applications, and 2) to build on this familiar foundation to segue into the study of quantum computing and its possible use cases. The presentation is technical but pragmatic and practice oriented. The authors cover theory to the necessary extent but largely proceed in an example-driven manner. Most of the examples are concerned with combinatorial optimization and consider problems that can be cast as quadratic unconstrained binary optimization problems.
Numerous python/numpy/scipy codes support the mathematical discussion and demonstrate how to put theory into practice, accompanied by exercises for each chapter. Parts of the material were adopted from long running lectures on pattern recognition, on the foundations of quantum computing, and on quantum computing algorithms, which are taught by the authors in the Computer Science master’s program at the University of Bonn.
